COVID-19 Testing in India – Retrospectively, seems like tested more wrong samples.

Author's view are personal and from his professional experience. mavuluri.pradeep@gmail.com

Tip (3), Arrow, columnar (binary file) formats for both R and Python

Though Arrow was for around from some time (more than a year), in its recent release they came up with compression support that makes it be more user friendly for all data science activities across different languages. In my earlier posts, I have encouraged R users to use fst, however, in recent days, as Data Scientists frequency use both R and Python, an effective small files that can be easily read and write are useful in both development and testing phase.

In, my current testings where we are using a specific customer data of size about > 3 GB (CSV) for developing and testing a new algorithm, recent “arrow” version's write to parquet has yielded in half the size of R’s fst when compressed with “gzip” compressor. Similarly, results are obtained even with python.

--- Happy R & Python programming let me know your experiences.

Views expressed here are from author’s industry experience. Author trains and blog’s on Machine (Deep) Learning applications with various programming languages; for further details, he will be available at mavuluri.pradeep@gmail.com for more details. Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Tip (2) for R to Python and Vice-Versa seamlessly

In continuation to my earlier R to Python tips, in order to deal with both Python and R simultaneously for client requests; this time with respect to plots where both schools as of now by large distinct in their plotting styles; Plotnine a new python package for grammar of graphics will help us with ease for two language enthusiasts.

Plotnine which is almost in same style (> 90%) as ggplot2, claims to be an "implementation of a grammar of graphics in Python, it is based on ggplot2 and allows users to compose plots by explicitly mapping data to the visual objects that make up the plot". Below is a quick example from the new API page:


## Installation
Conda - "conda install -c conda-forge plotnine"
(or)
PIP - "pip install plotnine"

## quick example from API page

from plotnine import ggplot, geom_point, aes, stat_smooth, facet_wrap
from plotnine.data import mtcars

(ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)'))
 + geom_point()
 + stat_smooth(method='lm')
 + facet_wrap('~gear'))

Those who have not yet experience this can try out now and let me know your experiences at mavuluri.pradeep@gmail.com

Happy R and Python Programming!

Machine Learning (ML) helps in not wasting time in non responsive telemarketing calls

My last year post was more about how ML helped in identifying best time for telemarketing calls. This one on not wasting time in non responsive telemarketing calls.

Views expressed here are from author’s industry experience. Author trains on and blogs Machine (Deep) Learning applications; for further details, he will be available at mavuluri.pradeep@gmail.com for more details. Find more about author at http://in.linkedin.com/in/pradeepmavuluri